Abstract
Mixing models have become increasingly common tools for apportioning fluvial sediment load to various sediment sources across catchments using a wide variety of Bayesian and frequentist modeling approaches. In this study, we demonstrate how different model setups can impact upon resulting source apportionment estimates in a Bayesian framework via a one-factor-at-a-time (OFAT) sensitivity analysis. We formulate 13 versions of a mixing model, each with different error assumptions and model structural choices, and apply them to sediment geochemistry data from the River Blackwater, Norfolk, UK, to apportion suspended particulate matter (SPM) contributions from three sources (arable topsoils, road verges, and subsurface material) under base flow conditions between August 2012 and August 2013. Whilst all 13 models estimate subsurface sources to be the largest contributor of SPM (median ∼76%), comparison of apportionment estimates reveal varying degrees of sensitivity to changing priors, inclusion of covariance terms, incorporation of time-variant distributions, and methods of proportion characterization. We also demonstrate differences in apportionment results between a full and an empirical Bayesian setup, and between a Bayesian and a frequentist optimization approach. This OFAT sensitivity analysis reveals that mixing model structural choices and error assumptions can significantly impact upon sediment source apportionment results, with estimated median contributions in this study varying by up to 21% between model versions. Users of mixing models are therefore strongly advised to carefully consider and justify their choice of model structure prior to conducting sediment source apportionment investigations.Key PointsAn OFAT sensitivity analysis of sediment fingerprinting mixing models is conductedBayesian models display high sensitivity to error assumptions and structural choicesSource apportionment results differ between Bayesian and frequentist approaches
Highlights
Source apportionment mixing models have been employed across a range of scientific disciplines to estimate the proportions of various sources that feed into a particular mixture or ‘‘target’’ of interest
Apportioning Sources of suspended particulate matter (SPM) The source apportionment estimates of model 1 (M1) reveal subsurface material to be the dominant source of SPM under base flow conditions throughout the period from August 2012 to August 2013 (Figure 4)
Estimated median sediment contributions derived from the subsurface source areas vary between 71 and 80% (51–92% at the 95% credible interval), with median contributions of 6–9% (1–27%) for arable topsoils and 12–17% (4–38%) for road verges
Summary
Source apportionment mixing models have been employed across a range of scientific disciplines to estimate the proportions of various sources that feed into a particular mixture or ‘‘target’’ of interest They are all based on the fundamental assumption that the composition of the target being studied, whether that be hair samples from mammals [Darimont et al, 2009] or sediment from rivers [Thompson et al, 2013], is a function of the composition of potential sources multiplied by their proportional contribution to the target. These frequentist models commonly minimize the sum of squared residuals as outlined by Collins et al [1997], with more recent approaches typically coupling parameter optimization with Monte Carlo based stochastic sampling to represent uncertainties associated with source area and target sediment variability [Collins et al, 2013; COOPER ET AL
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
